We run word2vec [1]} on the NYT corpus to obtain the initial word embeddings with dimension \(d_w=50\)  and update the embeddings during training. We set the dimension of entity token indicator embedding vector \(d_z=10\)  and positional embedding vector \(d_u=5\) . The hidden layer dimension of the forward and backward LSTM is 60, which is the same as the dimension of input word representation vector \(\mathbf {x}\) . The dimension of Bi-LSTM output is 120. We use \(f_g=f_e=230\)  filters of width \(k=3\)  for feature extraction whenever we apply the convolution operation. The size of the word embeddings, number of convolution filters, and the filter size are taken from the literature. We use dropout in our network with a dropout rate of \(0.5\) , and in convolutional layers, we use the tanh activation function. We use the sequence of tokens starting from 5 words before the entity to 5 words after the entity as its context. We train our models using mini-batches of size 50 and optimize the network parameters using the Adagrad optimizer [2]}. We use the dependency parser from spaCyhttps://spacy.io/ to obtain the dependency distance of the words from the entities and use \(ws=5\)  as the window size for dependency distance-based attention.
